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2026/2027  KAN-CINTO4003U  Artificial Intelligence and Machine Learning

English Title
Artificial Intelligence and Machine Learning

Course information
Language English
Course ECTS 7.5 ECTS
Type Mandatory
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for Digitalisation, Technology and Communication
Programme MSc in Business Administration and Information Systems
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Innovation
Teaching methods
  • Blended learning
Last updated on 29-06-2026

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Demonstrate the ability to understand and implement artificial intelligence technologies such as neural networks and deep learning, primarily as applied to natural language processing and image processing.
  • Understand basic principles of machine learning, including assessment of results using a variety of metrics, model tuning, and prompt engineering and analysis of large language models.
  • Demonstrate a practical ability to apply these machine learning techniques to relevant business cases.
  • Reflect on the societal and business impact of AI technologies.
Course prerequisites
Basic programming skills and basic knowledge of machine learning, including standard models for classification and regression
Examination
Artificial Intelligence and Machine Learning:
Exam ECTS 7,5
Examination form Active participation

The completion of this course is based on active student participation in class. The course will be considered as passed if the students participation - based on an overall assessment - in the class activities fulfill the learning objectives of the course. The individual student’s participation is assessed by the teacher.
The student must participate in A combination of assignment and presentation, Assignment(s)
Individual or group exam Individual exam
Grading scale Pass / Fail
Examiner(s) Assessed solely by the teacher
Exam period Summer
Make-up exam/re-exam Oral exam based on written product
In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance.
Size of written product: Max. 15 pages
Assignment type: Project
Duration: 15 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Examiner(s): If it is an internal examination, there will be a second internal examiner at the re-exam. If it is an external examination, there will be an external examiner.
Description of activities
A combination of assignment and presentation: Students will do a project (max. 15 pages) either individually og in group2 (2-4 students). They will write a paper and make a presentation of their results at the end of the semester
Assignment(s): Students will present a project plan at the middle of the semester. Also students will do weekly quizzes (9 in total) and group discussions where they post the results of the discussion. They also participate in weekly lab sessions where they submit the results of the lab.
Course content, structure and pedagogical approach

AI is poised to transform the business and technology landscape, and it has become essential for business leaders to understand the key technologies and concepts involved. This course covers several of the main AI technologies, including natural language processing and image recognition.  The primary focus is technical, and students are expected to be able to program in Python or a similar language, and to be familiar with machine learning techniques such as classification and regression. 

 

The course addresses several key aspects of the Nordic Nine -- especially under Knowledge ("analytical with data and curious about ambiguity" and "deep
business knowledge placed in a broad context").

 

Students are expected to work with large language models and other forms of

generative AI in exercises, assignments, and exams. As with any other software, it should be clearly stated how the AI models are used in the performance of a given exercise, assignment, or exam.

 
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Activities that contribute to new or existing research projects
  • Students conduct independent research-like activities under supervision
Description of the teaching methods
Lectures and weekly hands-on sessions with practical exercises.
Feedback during the teaching period
Students have hands-on exercises each week, where they receive in-person feedback from the teacher. They also receive feedback from regular online elements such as quizzes. Furthermore, they receive weekly written feedback on their work. Mid-way through the course, they create a plan for their project, and they receive feedback from the professor on their plan. There are also weekly office hours.
Student workload
Lectures 20 hours
Exercises 10 hours
Class Preparation 116 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the literature on Canvas before they buy the books.

 

Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists.  O'Reilly Media, Inc.

 
Last updated on 29-06-2026